{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Practical Statistics for Data Scientists (Python)\n",
    "# Chapter 1. Exploratory Data Analysis\n",
    "> (c) 2019 Peter C. Bruce, Andrew Bruce, Peter Gedeck"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import required Python packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from pathlib import Path\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import trim_mean\n",
    "from statsmodels import robust\n",
    "import wquantiles\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pylab as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define paths to data sets. If you don't keep your data in the same directory as the code, adapt the path names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA = Path('.').resolve().parents[1] / 'data'\n",
    "\n",
    "AIRLINE_STATS_CSV = DATA / 'airline_stats.csv'\n",
    "KC_TAX_CSV = DATA / 'kc_tax.csv.gz'\n",
    "LC_LOANS_CSV = DATA / 'lc_loans.csv'\n",
    "AIRPORT_DELAYS_CSV = DATA / 'dfw_airline.csv'\n",
    "SP500_DATA_CSV = DATA / 'sp500_data.csv.gz'\n",
    "SP500_SECTORS_CSV = DATA / 'sp500_sectors.csv'\n",
    "STATE_CSV = DATA / 'state.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimates of Location\n",
    "## Example: Location Estimates of Population and Murder Rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         State  Population  Murder.Rate Abbreviation\n",
      "0      Alabama     4779736          5.7           AL\n",
      "1       Alaska      710231          5.6           AK\n",
      "2      Arizona     6392017          4.7           AZ\n",
      "3     Arkansas     2915918          5.6           AR\n",
      "4   California    37253956          4.4           CA\n",
      "5     Colorado     5029196          2.8           CO\n",
      "6  Connecticut     3574097          2.4           CT\n",
      "7     Delaware      897934          5.8           DE\n"
     ]
    }
   ],
   "source": [
    "# Table 1-2\n",
    "state = pd.read_csv(STATE_CSV)\n",
    "print(state.head(8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Compute the mean, trimmed mean, and median for Population. For `mean` and `median` we can use the _pandas_ methods of the data frame. The trimmed mean requires the `trim_mean` function in _scipy.stats_."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6162876.3\n"
     ]
    }
   ],
   "source": [
    "state = pd.read_csv(STATE_CSV)\n",
    "print(state['Population'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4783697.125\n"
     ]
    }
   ],
   "source": [
    "print(trim_mean(state['Population'], 0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4436369.5\n"
     ]
    }
   ],
   "source": [
    "print(state['Population'].median())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weighted mean is available with numpy. For weighted median, we can use the specialised package `wquantiles` (https://pypi.org/project/wquantiles/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.066\n"
     ]
    }
   ],
   "source": [
    "print(state['Murder.Rate'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.445833981123393\n"
     ]
    }
   ],
   "source": [
    "print(np.average(state['Murder.Rate'], weights=state['Population']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.4\n"
     ]
    }
   ],
   "source": [
    "print(wquantiles.median(state['Murder.Rate'], weights=state['Population']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Estimates of Variability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         State  Population  Murder.Rate Abbreviation\n",
      "0      Alabama     4779736          5.7           AL\n",
      "1       Alaska      710231          5.6           AK\n",
      "2      Arizona     6392017          4.7           AZ\n",
      "3     Arkansas     2915918          5.6           AR\n",
      "4   California    37253956          4.4           CA\n",
      "5     Colorado     5029196          2.8           CO\n",
      "6  Connecticut     3574097          2.4           CT\n",
      "7     Delaware      897934          5.8           DE\n"
     ]
    }
   ],
   "source": [
    "# Table 1-2\n",
    "print(state.head(8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6848235.347401142\n"
     ]
    }
   ],
   "source": [
    "print(state['Population'].std())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interquartile range is calculated as the difference of the 75% and 25% quantile."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4847308.0\n"
     ]
    }
   ],
   "source": [
    "print(state['Population'].quantile(0.75) - state['Population'].quantile(0.25))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Median absolute deviation from the median can be calculated with a method in _statsmodels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3849876.1459979336\n",
      "3849876.1459979336\n"
     ]
    }
   ],
   "source": [
    "print(robust.scale.mad(state['Population']))\n",
    "print(abs(state['Population'] - state['Population'].median()).median() / 0.6744897501960817)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Percentiles and Boxplots\n",
    "_Pandas_ has the `quantile` method for data frames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05    1.600\n",
      "0.25    2.425\n",
      "0.50    4.000\n",
      "0.75    5.550\n",
      "0.95    6.510\n",
      "Name: Murder.Rate, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(state['Murder.Rate'].quantile([0.05, 0.25, 0.5, 0.75, 0.95]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             5.0%  25.0%  50.0%  75.0%  95.0%\n",
      "Murder.Rate   1.6  2.425    4.0   5.55   6.51\n"
     ]
    }
   ],
   "source": [
    "# Table 1.4\n",
    "percentages = [0.05, 0.25, 0.5, 0.75, 0.95]\n",
    "df = pd.DataFrame(state['Murder.Rate'].quantile(percentages))\n",
    "df.index = [f'{p * 100}%' for p in percentages]\n",
    "print(df.transpose())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Pandas_ provides a number of basic exploratory plots; one of them are boxplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (state['Population']/1_000_000).plot.box(figsize=(3, 4))\n",
    "ax.set_ylabel('Population (millions)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency Table and Histograms\n",
    "The `cut` method for _pandas_ data splits the dataset into bins. There are a number of arguments for the method. The following code creates equal sized bins. The method `value_counts` returns a frequency table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(526935.67, 4232659.0]      24\n",
      "(4232659.0, 7901692.0]      14\n",
      "(7901692.0, 11570725.0]      6\n",
      "(11570725.0, 15239758.0]     2\n",
      "(33584923.0, 37253956.0]     1\n",
      "(22577824.0, 26246857.0]     1\n",
      "(18908791.0, 22577824.0]     1\n",
      "(15239758.0, 18908791.0]     1\n",
      "(29915890.0, 33584923.0]     0\n",
      "(26246857.0, 29915890.0]     0\n",
      "Name: Population, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "binnedPopulation = pd.cut(state['Population'], 10)\n",
    "print(binnedPopulation.value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   BinRange  Count  \\\n",
      "0    (526935.67, 4232659.0]     24   \n",
      "1    (4232659.0, 7901692.0]     14   \n",
      "2   (7901692.0, 11570725.0]      6   \n",
      "3  (11570725.0, 15239758.0]      2   \n",
      "4  (15239758.0, 18908791.0]      1   \n",
      "5  (18908791.0, 22577824.0]      1   \n",
      "6  (22577824.0, 26246857.0]      1   \n",
      "7  (26246857.0, 29915890.0]      0   \n",
      "8  (29915890.0, 33584923.0]      0   \n",
      "9  (33584923.0, 37253956.0]      1   \n",
      "\n",
      "                                              States  \n",
      "0  WY,VT,ND,AK,SD,DE,MT,RI,NH,ME,HI,ID,NE,WV,NM,N...  \n",
      "1          KY,LA,SC,AL,CO,MN,WI,MD,MO,TN,AZ,IN,MA,WA  \n",
      "2                                  VA,NJ,NC,GA,MI,OH  \n",
      "3                                              PA,IL  \n",
      "4                                                 FL  \n",
      "5                                                 NY  \n",
      "6                                                 TX  \n",
      "7                                                     \n",
      "8                                                     \n",
      "9                                                 CA  \n"
     ]
    }
   ],
   "source": [
    "# Table 1.5\n",
    "binnedPopulation.name = 'binnedPopulation'\n",
    "df = pd.concat([state, binnedPopulation], axis=1)\n",
    "df = df.sort_values(by='Population')\n",
    "\n",
    "groups = []\n",
    "for group, subset in df.groupby(by='binnedPopulation'):\n",
    "    groups.append({\n",
    "        'BinRange': group,\n",
    "        'Count': len(subset),\n",
    "        'States': ','.join(subset.Abbreviation)\n",
    "    })\n",
    "print(pd.DataFrame(groups))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Pandas_ also supports histograms for exploratory data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = (state['Population'] / 1_000_000).plot.hist(figsize=(4, 4))\n",
    "ax.set_xlabel('Population (millions)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Density Estimates\n",
    "Density is an alternative to histograms that can provide more insight into the distribution of the data points. Use the argument `bw_method` to control the smoothness of the density curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = state['Murder.Rate'].plot.hist(density=True, xlim=[0, 12], \n",
    "                                    bins=range(1,12), figsize=(4, 4))\n",
    "state['Murder.Rate'].plot.density(ax=ax)\n",
    "ax.set_xlabel('Murder Rate (per 100,000)')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Binary and Categorical Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Carrier        ATC   Weather  Security    Inbound\n",
      "0  23.022989  30.400781  4.025214  0.122937  42.428079\n"
     ]
    }
   ],
   "source": [
    "# Table 1-6\n",
    "dfw = pd.read_csv(AIRPORT_DELAYS_CSV)\n",
    "print(100 * dfw / dfw.values.sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Pandas_ also supports bar charts for displaying a single categorical variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = dfw.transpose().plot.bar(figsize=(4, 4), legend=False)\n",
    "ax.set_xlabel('Cause of delay')\n",
    "ax.set_ylabel('Count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation\n",
    "First read the required datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "sp500_sym = pd.read_csv(SP500_SECTORS_CSV)\n",
    "sp500_px = pd.read_csv(SP500_DATA_CSV, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   T       CTL       FTR        VZ      LVLT\n",
      "2012-07-02  0.422496  0.140847  0.070879  0.554180 -0.519998\n",
      "2012-07-03 -0.177448  0.066280  0.070879 -0.025976 -0.049999\n",
      "2012-07-05 -0.160548 -0.132563  0.055128 -0.051956 -0.180000\n",
      "2012-07-06  0.342205  0.132563  0.007875  0.140106 -0.359999\n",
      "2012-07-09  0.136883  0.124279 -0.023626  0.253943  0.180000\n",
      "...              ...       ...       ...       ...       ...\n",
      "2015-06-25  0.049342 -1.600000 -0.040000 -0.187790 -0.330002\n",
      "2015-06-26 -0.256586  0.039999 -0.070000  0.029650 -0.739998\n",
      "2015-06-29 -0.098685 -0.559999 -0.060000 -0.504063 -1.360000\n",
      "2015-06-30 -0.503298 -0.420000 -0.070000 -0.523829  0.199997\n",
      "2015-07-01 -0.019737  0.080000 -0.050000  0.355811  0.139999\n",
      "\n",
      "[754 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "# Table 1-7\n",
    "# Determine telecommunications symbols\n",
    "telecomSymbols = sp500_sym[sp500_sym['sector'] == 'telecommunications_services']['symbol']\n",
    "\n",
    "# Filter data for dates July 2012 through June 2015\n",
    "telecom = sp500_px.loc[sp500_px.index >= '2012-07-01', telecomSymbols]\n",
    "telecom.corr()\n",
    "print(telecom)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we focus on funds traded on major exchanges (sector == 'etf'). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 XLI       QQQ       SPY       DIA       GLD    VXX       USO  \\\n",
      "2012-07-02 -0.376098  0.096313  0.028223 -0.242796  0.419998 -10.40  0.000000   \n",
      "2012-07-03  0.376099  0.481576  0.874936  0.728405  0.490006  -3.52  0.250000   \n",
      "2012-07-05  0.150440  0.096313 -0.103487  0.149420  0.239991   6.56 -0.070000   \n",
      "2012-07-06 -0.141040 -0.491201  0.018819 -0.205449 -0.519989  -8.80 -0.180000   \n",
      "2012-07-09  0.244465 -0.048160 -0.056445 -0.168094  0.429992  -0.48  0.459999   \n",
      "\n",
      "                 IWM       XLE       XLY       XLU       XLB       XTL  \\\n",
      "2012-07-02  0.534641  0.028186  0.095759  0.098311 -0.093713  0.019076   \n",
      "2012-07-03  0.926067  0.995942  0.000000 -0.044686  0.337373  0.000000   \n",
      "2012-07-05 -0.171848 -0.460387  0.306431 -0.151938  0.103086  0.019072   \n",
      "2012-07-06 -0.229128  0.206706  0.153214  0.080437  0.018744 -0.429213   \n",
      "2012-07-09 -0.190939 -0.234892 -0.201098 -0.035751 -0.168687  0.000000   \n",
      "\n",
      "                 XLV       XLP       XLF       XLK  \n",
      "2012-07-02 -0.009529  0.313499  0.018999  0.075668  \n",
      "2012-07-03  0.000000  0.129087  0.104492  0.236462  \n",
      "2012-07-05 -0.142955 -0.073766 -0.142490  0.066211  \n",
      "2012-07-06 -0.095304  0.119865  0.066495 -0.227003  \n",
      "2012-07-09  0.352630 -0.064548  0.018999  0.009457  \n"
     ]
    }
   ],
   "source": [
    "etfs = sp500_px.loc[sp500_px.index > '2012-07-01', \n",
    "                    sp500_sym[sp500_sym['sector'] == 'etf']['symbol']]\n",
    "print(etfs.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to the large number of columns in this table, looking at the correlation matrix is cumbersome and it's more convenient to plot the correlation as a heatmap. The _seaborn_ package provides a convenient implementation for heatmaps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 4))\n",
    "ax = sns.heatmap(etfs.corr(), vmin=-1, vmax=1, \n",
    "                 cmap=sns.diverging_palette(20, 220, as_cmap=True),\n",
    "                 ax=ax)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above heatmap works when you have color. For the greyscale images, as used in the book, we need to visualize the direction as well. The following code shows the strength of the correlation using ellipses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.collections import EllipseCollection\n",
    "from matplotlib.colors import Normalize\n",
    "\n",
    "def plot_corr_ellipses(data, figsize=None, **kwargs):\n",
    "    ''' https://stackoverflow.com/a/34558488 '''\n",
    "    M = np.array(data)\n",
    "    if not M.ndim == 2:\n",
    "        raise ValueError('data must be a 2D array')\n",
    "    fig, ax = plt.subplots(1, 1, figsize=figsize, subplot_kw={'aspect':'equal'})\n",
    "    ax.set_xlim(-0.5, M.shape[1] - 0.5)\n",
    "    ax.set_ylim(-0.5, M.shape[0] - 0.5)\n",
    "    ax.invert_yaxis()\n",
    "\n",
    "    # xy locations of each ellipse center\n",
    "    xy = np.indices(M.shape)[::-1].reshape(2, -1).T\n",
    "\n",
    "    # set the relative sizes of the major/minor axes according to the strength of\n",
    "    # the positive/negative correlation\n",
    "    w = np.ones_like(M).ravel() + 0.01\n",
    "    h = 1 - np.abs(M).ravel() - 0.01\n",
    "    a = 45 * np.sign(M).ravel()\n",
    "\n",
    "    ec = EllipseCollection(widths=w, heights=h, angles=a, units='x', offsets=xy,\n",
    "                           norm=Normalize(vmin=-1, vmax=1),\n",
    "                           transOffset=ax.transData, array=M.ravel(), **kwargs)\n",
    "    ax.add_collection(ec)\n",
    "\n",
    "    # if data is a DataFrame, use the row/column names as tick labels\n",
    "    if isinstance(data, pd.DataFrame):\n",
    "        ax.set_xticks(np.arange(M.shape[1]))\n",
    "        ax.set_xticklabels(data.columns, rotation=90)\n",
    "        ax.set_yticks(np.arange(M.shape[0]))\n",
    "        ax.set_yticklabels(data.index)\n",
    "\n",
    "    return ec\n",
    "\n",
    "m = plot_corr_ellipses(etfs.corr(), figsize=(5, 4), cmap='bwr_r')\n",
    "cb = fig.colorbar(m)\n",
    "cb.set_label('Correlation coefficient')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatterplots\n",
    "Simple scatterplots are supported by _pandas_. Specifying the marker as `$\\u25EF$` uses an open circle for each point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = telecom.plot.scatter(x='T', y='VZ', figsize=(4, 4), marker='$\\u25EF$')\n",
    "ax.set_xlabel('ATT (T)')\n",
    "ax.set_ylabel('Verizon (VZ)')\n",
    "ax.axhline(0, color='grey', lw=1)\n",
    "ax.axvline(0, color='grey', lw=1)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Line2D(_line1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = telecom.plot.scatter(x='T', y='VZ', figsize=(4, 4), marker='$\\u25EF$', alpha=0.5)\n",
    "ax.set_xlabel('ATT (T)')\n",
    "ax.set_ylabel('Verizon (VZ)')\n",
    "ax.axhline(0, color='grey', lw=1)\n",
    "print(ax.axvline(0, color='grey', lw=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Two or More Variables\n",
    "Load the kc_tax dataset and filter based on a variety of criteria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(432693, 3)\n"
     ]
    }
   ],
   "source": [
    "kc_tax = pd.read_csv(KC_TAX_CSV)\n",
    "kc_tax0 = kc_tax.loc[(kc_tax.TaxAssessedValue < 750000) & \n",
    "                     (kc_tax.SqFtTotLiving > 100) &\n",
    "                     (kc_tax.SqFtTotLiving < 3500), :]\n",
    "print(kc_tax0.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hexagonal binning and Contours \n",
    "### Plotting numeric versus numeric data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the number of data points gets large, scatter plots will no longer be meaningful. Here methods that visualize densities are more useful. The `hexbin` method for _pandas_ data frames is one powerful approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = kc_tax0.plot.hexbin(x='SqFtTotLiving', y='TaxAssessedValue',\n",
    "                         gridsize=30, sharex=False, figsize=(5, 4))\n",
    "ax.set_xlabel('Finished Square Feet')\n",
    "ax.set_ylabel('Tax Assessed Value')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The _seaborn_ kdeplot is a two-dimensional extension of the density plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(4, 4))\n",
    "ax = sns.kdeplot(kc_tax0.SqFtTotLiving, kc_tax0.TaxAssessedValue, ax=ax)\n",
    "ax.set_xlabel('Finished Square Feet')\n",
    "ax.set_ylabel('Tax Assessed Value')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two Categorical Variables\n",
    "Load the `lc_loans` dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "lc_loans = pd.read_csv(LC_LOANS_CSV)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "status  Charged Off  Current  Fully Paid  Late     All\n",
      "grade                                                 \n",
      "A              1562    50051       20408   469   72490\n",
      "B              5302    93852       31160  2056  132370\n",
      "C              6023    88928       23147  2777  120875\n",
      "D              5007    53281       13681  2308   74277\n",
      "E              2842    24639        5949  1374   34804\n",
      "F              1526     8444        2328   606   12904\n",
      "G               409     1990         643   199    3241\n",
      "All           22671   321185       97316  9789  450961\n"
     ]
    }
   ],
   "source": [
    "# Table 1-8(1)\n",
    "crosstab = lc_loans.pivot_table(index='grade', columns='status', \n",
    "                                aggfunc=lambda x: len(x), margins=True)\n",
    "print(crosstab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "status  Charged Off   Current  Fully Paid      Late       All\n",
      "grade                                                        \n",
      "A          0.021548  0.690454    0.281528  0.006470  0.160746\n",
      "B          0.040054  0.709013    0.235401  0.015532  0.293529\n",
      "C          0.049828  0.735702    0.191495  0.022974  0.268039\n",
      "D          0.067410  0.717328    0.184189  0.031073  0.164708\n",
      "E          0.081657  0.707936    0.170929  0.039478  0.077177\n",
      "F          0.118258  0.654371    0.180409  0.046962  0.028614\n",
      "G          0.126196  0.614008    0.198396  0.061401  0.007187\n"
     ]
    }
   ],
   "source": [
    "# Table 1-8(2)\n",
    "df = crosstab.copy().loc['A':'G',:]\n",
    "df.loc[:,'Charged Off':'Late'] = df.loc[:,'Charged Off':'Late'].div(df['All'], axis=0)\n",
    "df['All'] = df['All'] / sum(df['All'])\n",
    "perc_crosstab = df\n",
    "print(perc_crosstab)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical and Numeric Data\n",
    "_Pandas_ boxplots of a column can be grouped by a different column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "airline_stats = pd.read_csv(AIRLINE_STATS_CSV)\n",
    "airline_stats.head()\n",
    "ax = airline_stats.boxplot(by='airline', column='pct_carrier_delay',\n",
    "                           figsize=(5, 5))\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('Daily % of Delayed Flights')\n",
    "plt.suptitle('')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Pandas_ also supports a variation of boxplots called _violinplot_. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "sns.violinplot(airline_stats.airline, airline_stats.pct_carrier_delay,\n",
    "               ax=ax, inner='quartile', color='white')\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('Daily % of Delayed Flights')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Multiple Variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "zip_codes = [98188, 98105, 98108, 98126]\n",
    "kc_tax_zip = kc_tax0.loc[kc_tax0.ZipCode.isin(zip_codes),:]\n",
    "kc_tax_zip\n",
    "\n",
    "def hexbin(x, y, color, **kwargs):\n",
    "    cmap = sns.light_palette(color, as_cmap=True)\n",
    "    plt.hexbin(x, y, gridsize=25, cmap=cmap, **kwargs)\n",
    "\n",
    "g = sns.FacetGrid(kc_tax_zip, col='ZipCode', col_wrap=2)\n",
    "g.map(hexbin, 'SqFtTotLiving', 'TaxAssessedValue', \n",
    "      extent=[0, 3500, 0, 700000])\n",
    "g.set_axis_labels('Finished Square Feet', 'Tax Assessed Value')\n",
    "g.set_titles('Zip code {col_name:.0f}')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
